Evaluation and Intercomparison of Five North American Dry Deposition Algorithms at a Mixed Forest Site
Why this work is in the frame
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Bibliographic record
Abstract
Abstract To quantify differences between dry deposition algorithms commonly used in North America, five models were selected to calculate dry deposition velocity ( V d ) for O 3 and SO 2 over a temperate mixed forest in southern Ontario, Canada, where a 5‐year flux database had previously been developed. The models performed better in summer than in winter with correlation coefficients for hourly V d between models and measurements being approximately 0.6 and 0.3, respectively. Differences in mean V d values between models were on the order of a factor of 2 in both summer and winter. All models produced lower V d values than the measurements of O 3 in summer and SO 2 in summer and winter, although the measured V d may be biased. There was not a consistent tendency in the models to overpredict or underpredict for O 3 in winter. Several models produced magnitudes of the diel variation of V d (O 3 ) comparable to the measurements, while all models produced slightly smaller diel variations than the measurements of V d (SO 2 ) in summer. A few models produced larger diel variations than the measurements of V d for O 3 and SO 2 in winter. Model differences were mainly due to different surface resistance parameterizations for stomatal and nonstomatal uptake pathways, while differences in aerodynamic and quasi‐laminar resistances played only a minor role. It is recommended to use ensemble modeling results for ecosystem impact assessment studies, which provides mean values of all the used models and thus can avoid too much overestimations or underestimations.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it